Digital Oncology Insights: January 8 - January 14

 

Mount Sinai’s new AI replaces weeks of manual review, scanning records in real-time to find trial patients instantly.

A leading cancer center in New York has deployed a specialized artificial intelligence platform to solve the persistent problem of low enrollment in clinical trials. The system uses a large language model trained specifically on oncology data to scan electronic health records in real time. Unlike general AI tools this model understands complex cancer terminology including specific biomarkers and treatment histories hidden in unstructured doctor notes. By automating the screening process the technology identifies eligible patients the moment they qualify replacing the slow and fragmented manual review that often causes patients to miss out on experimental therapies. Clinicians report that the tool drastically reduces the administrative burden allowing them to focus on discussing meaningful treatment options with patients rather than sifting through paperwork. This deployment marks a significant step toward making access to advanced cancer research faster and more equitable.

Read the original article at: https://hitconsultant.net/2026/01/08/mount-sinai-deploys-ai-powered-clinical-trial-matching-platform-to-expand-access-to-cancer-research/

The system is too slow. New data reveals that missed "waiting time" targets are hiding the true, deadly cost of cancer care.

A critical new analysis argues that current targets for cancer treatment waiting times are failing patients and obscuring the true extent of delays. The report highlights that arbitrary benchmarks such as the 62 day target from referral to treatment are frequently missed and do not account for the increasing complexity of modern cancer care. Patients requiring multiple diagnostic tests often face much longer waits that are not accurately reflected in official data. These delays are not merely administrative nuisances they significantly increase the risk of mortality. The authors contend that simply setting stricter targets is ineffective without addressing the underlying lack of resources. Instead they call for a national learning system driven by data and collaboration to identify bottlenecks in real time and prioritize patients based on clinical urgency rather than outdated metrics.

Read the original article at: http://www.bmj.com/content/392/bmj.s14.short?rss=1

Robotic guidance cuts procedure time and radiation exposure in half, making lung tumor ablation faster and safer.

For patients with inoperable lung cancer radiofrequency ablation offers a lifeline but its success depends heavily on the precise placement of needles. A new study demonstrates that using robotic assistance can significantly improve the safety and efficiency of this delicate procedure. Researchers compared standard manual techniques against a robot guided system that uses artificial intelligence for real time motion tracking. The results showed that the robot helped doctors position the probe with far greater accuracy while reducing the time needed for needle insertion by several minutes. Most importantly the robotic approach cut the duration of CT scans and the resulting radiation exposure to the patient by nearly fifty percent. This finding suggests that integrating robotics into interventional radiology not only standardizes outcomes but also protects vulnerable patients from unnecessary radiation risks during treatment.

Read the original article at: https://radiologybusiness.com/topics/medical-imaging/interventional-radiology/robotic-assisted-navigation-improves-accuracy-halves-radiation-dose-during-interventional-procedures

CRISPR allows researchers to edit the genetic source code quicker and cheaper than ever before.

The gene editing tool CRISPR has fundamentally transformed cancer research by acting as molecular scissors that can cut and modify DNA with unprecedented ease. Since its introduction the technology has allowed scientists to deactivate specific genes or introduce new DNA sequences much faster and cheaper than older methods allowed. This efficiency is accelerating the development of next generation therapies including CAR T cells that are engineered to hunt down cancer more effectively. Researchers are currently using the tool to create more accurate mouse models of human cancer and to identify the genetic drivers of tumor growth. While challenges remain regarding how to deliver the tool safely into the human body without affecting healthy cells the technology offers a promising path toward treating cancer at its genetic root rather than just managing symptoms.

Read the original article at: https://www.cancer.gov/news-events/cancer-currents-blog/2020/crispr-cancer-research-treatment

 

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